stock_deeplearning

this project is developing to used deep-learning to train stock base on tensorflow

env requirements, 1, tushare 2, tensorflow

volme and price trade rule

run step,

1. cd stock_process_day_data
2. python getdata.py (get all the A stock day trade data)
3. cd easyvolumeprice
4. python easyvolumeprice (run volum and price trade strategy)

technical analysis back test

1. cd stock_process_day_data
2. python getdata.py (get all the A stock day trade data)
3. cd easyvolumeprice
4. python trade_ta_back_test (run macd buy and kdj sell strategy)

deep learning price predict

simple user guide, without any modification, you can training 000001 stock price rate with stock('000001','000002','000018', '600000','600005','600007') close, open, high, low, volum data.

1. cd stock_process_day_data
2. python getdata.py (get all the A stock day trade data) 3). cd /stock_deeplearning 4). python train.py